Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7fcd8f2dfa90>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 16

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7fcd8f1d4e10>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    image_input = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels])
    z_input = tf.placeholder(tf.float32, [None, z_dim])
    learning_rate = tf.placeholder(tf.float32, name="learning_rate")

    return image_input, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        ###### trying dropout
        drop = tf.layers.dropout(relu3, 0.75)

        flat = tf.reshape(drop, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.2
    with tf.variable_scope('generator', reuse= not is_train):
        
        x1 = tf.layers.dense(z, 7*7*512)
        
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        bn1 = tf.layers.batch_normalization(x1, training=is_train)
        relu1 = tf.maximum(alpha * bn1, bn1)
        
        x2 = tf.layers.conv2d_transpose(relu1, 256, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d_transpose(relu2, 128, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=is_train)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 5, strides=1, padding='same')
        
        out = tf.tanh(logits)   
        
    return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """

    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
                        logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
                        logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
                        logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
   
    t_vars = tf.trainable_variables()    
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    d_train_opt = tf.train.AdamOptimizer(
        learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    g_updates = [opt for opt in update_ops if opt.name.startswith('generator')]
    with tf.control_dependencies(g_updates):
        g_train_opt = tf.train.AdamOptimizer(
            learning_rate, beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    #tf.reset_default_graph()
        
    image_width = data_shape[1]
    image_height = data_shape[2]
    image_channels = data_shape[3]
    
    input_real, input_z, _ = model_inputs(image_width, image_height, image_channels, z_dim)
   
    l_rate = learning_rate

    #loss, optimization
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, l_rate, beta1) 
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        i = 0
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                i += 1
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))   
                batch_images = batch_images*2
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z})
                 
                train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
                train_loss_g = g_loss.eval({input_z: batch_z})
                
                print("Epoch {}/{}...".format(epoch_i+1, epoch_count),"Discriminator Loss: {:.4f}...".format(train_loss_d),"Generator Loss: {:.4f}".format(train_loss_g))
                
                if i % 10 == 0:
                    show_generator_output(sess, 16, input_z, image_channels, data_image_mode)

                    
        return None

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 64
z_dim = 100
learning_rate = 0.0004
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, 
          learning_rate, beta1, mnist_dataset.get_batches, 
          mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 1/2... Discriminator Loss: 7.4633... Generator Loss: 0.0008
Epoch 1/2... Discriminator Loss: 3.9193... Generator Loss: 0.0431
Epoch 1/2... Discriminator Loss: 0.1848... Generator Loss: 2.1995
Epoch 1/2... Discriminator Loss: 0.0437... Generator Loss: 6.9782
Epoch 1/2... Discriminator Loss: 0.0829... Generator Loss: 7.7844
Epoch 1/2... Discriminator Loss: 0.0378... Generator Loss: 4.7446
Epoch 1/2... Discriminator Loss: 0.0485... Generator Loss: 3.5549
Epoch 1/2... Discriminator Loss: 2.8198... Generator Loss: 0.0673
Epoch 1/2... Discriminator Loss: 0.2681... Generator Loss: 14.1404
Epoch 1/2... Discriminator Loss: 0.1281... Generator Loss: 16.9890

Epoch 1/2... Discriminator Loss: 0.0377... Generator Loss: 14.4848
Epoch 1/2... Discriminator Loss: 0.0052... Generator Loss: 10.2077
Epoch 1/2... Discriminator Loss: 0.0052... Generator Loss: 9.6322
Epoch 1/2... Discriminator Loss: 0.0015... Generator Loss: 10.1432
Epoch 1/2... Discriminator Loss: 0.0022... Generator Loss: 7.4836
Epoch 1/2... Discriminator Loss: 0.0057... Generator Loss: 8.3491
Epoch 1/2... Discriminator Loss: 0.0030... Generator Loss: 8.2572
Epoch 1/2... Discriminator Loss: 0.0014... Generator Loss: 7.9786
Epoch 1/2... Discriminator Loss: 0.0010... Generator Loss: 7.4010
Epoch 1/2... Discriminator Loss: 0.0024... Generator Loss: 7.2113

Epoch 1/2... Discriminator Loss: 0.0037... Generator Loss: 5.8639
Epoch 1/2... Discriminator Loss: 0.0184... Generator Loss: 4.0817
Epoch 1/2... Discriminator Loss: 0.0229... Generator Loss: 3.9046
Epoch 1/2... Discriminator Loss: 0.2379... Generator Loss: 1.7728
Epoch 1/2... Discriminator Loss: 0.0053... Generator Loss: 16.8411
Epoch 1/2... Discriminator Loss: 0.0127... Generator Loss: 19.4109
Epoch 1/2... Discriminator Loss: 0.0585... Generator Loss: 20.6117
Epoch 1/2... Discriminator Loss: 0.0096... Generator Loss: 19.8878
Epoch 1/2... Discriminator Loss: 0.0082... Generator Loss: 16.3149
Epoch 1/2... Discriminator Loss: 0.0016... Generator Loss: 14.4869

Epoch 1/2... Discriminator Loss: 0.0376... Generator Loss: 3.7658
Epoch 1/2... Discriminator Loss: 0.0030... Generator Loss: 7.1196
Epoch 1/2... Discriminator Loss: 0.0125... Generator Loss: 6.2810
Epoch 1/2... Discriminator Loss: 0.0809... Generator Loss: 4.0837
Epoch 1/2... Discriminator Loss: 0.0057... Generator Loss: 8.7050
Epoch 1/2... Discriminator Loss: 0.0100... Generator Loss: 6.4476
Epoch 1/2... Discriminator Loss: 0.0775... Generator Loss: 3.6236
Epoch 1/2... Discriminator Loss: 0.1392... Generator Loss: 2.7352
Epoch 1/2... Discriminator Loss: 0.0397... Generator Loss: 6.9371
Epoch 1/2... Discriminator Loss: 0.0317... Generator Loss: 6.6888

Epoch 1/2... Discriminator Loss: 0.0212... Generator Loss: 4.7813
Epoch 1/2... Discriminator Loss: 0.0697... Generator Loss: 3.1361
Epoch 1/2... Discriminator Loss: 0.4888... Generator Loss: 1.4463
Epoch 1/2... Discriminator Loss: 0.8878... Generator Loss: 9.7094
Epoch 1/2... Discriminator Loss: 1.3090... Generator Loss: 2.2392
Epoch 1/2... Discriminator Loss: 0.2471... Generator Loss: 6.7072
Epoch 1/2... Discriminator Loss: 0.0983... Generator Loss: 6.1434
Epoch 1/2... Discriminator Loss: 0.2081... Generator Loss: 3.3803
Epoch 1/2... Discriminator Loss: 0.1741... Generator Loss: 4.3009
Epoch 1/2... Discriminator Loss: 0.1302... Generator Loss: 3.2757

Epoch 1/2... Discriminator Loss: 0.1484... Generator Loss: 2.8371
Epoch 1/2... Discriminator Loss: 0.0805... Generator Loss: 3.6153
Epoch 1/2... Discriminator Loss: 0.2409... Generator Loss: 1.9448
Epoch 1/2... Discriminator Loss: 0.1206... Generator Loss: 4.8216
Epoch 1/2... Discriminator Loss: 0.6826... Generator Loss: 1.1120
Epoch 1/2... Discriminator Loss: 4.2597... Generator Loss: 12.1292
Epoch 1/2... Discriminator Loss: 9.4655... Generator Loss: 0.0100
Epoch 1/2... Discriminator Loss: 0.7887... Generator Loss: 2.2081
Epoch 1/2... Discriminator Loss: 0.9430... Generator Loss: 7.3800
Epoch 1/2... Discriminator Loss: 1.0198... Generator Loss: 1.8857

Epoch 1/2... Discriminator Loss: 0.8438... Generator Loss: 2.4609
Epoch 1/2... Discriminator Loss: 0.7739... Generator Loss: 2.5938
Epoch 1/2... Discriminator Loss: 0.9897... Generator Loss: 1.1884
Epoch 1/2... Discriminator Loss: 0.3791... Generator Loss: 3.4138
Epoch 1/2... Discriminator Loss: 0.5220... Generator Loss: 1.9404
Epoch 1/2... Discriminator Loss: 0.5105... Generator Loss: 4.0789
Epoch 1/2... Discriminator Loss: 1.3947... Generator Loss: 1.1290
Epoch 1/2... Discriminator Loss: 1.8092... Generator Loss: 10.3559
Epoch 1/2... Discriminator Loss: 0.0136... Generator Loss: 6.9263
Epoch 1/2... Discriminator Loss: 0.4232... Generator Loss: 1.9272

Epoch 1/2... Discriminator Loss: 0.8438... Generator Loss: 0.8750
Epoch 1/2... Discriminator Loss: 0.3013... Generator Loss: 8.2337
Epoch 1/2... Discriminator Loss: 0.3922... Generator Loss: 6.5859
Epoch 1/2... Discriminator Loss: 1.6826... Generator Loss: 0.5977
Epoch 1/2... Discriminator Loss: 1.0822... Generator Loss: 3.3196
Epoch 1/2... Discriminator Loss: 1.0538... Generator Loss: 1.4452
Epoch 1/2... Discriminator Loss: 0.9492... Generator Loss: 1.0493
Epoch 1/2... Discriminator Loss: 0.4495... Generator Loss: 3.9401
Epoch 1/2... Discriminator Loss: 0.3600... Generator Loss: 2.0015
Epoch 1/2... Discriminator Loss: 0.5248... Generator Loss: 1.5322

Epoch 1/2... Discriminator Loss: 0.3311... Generator Loss: 3.1447
Epoch 1/2... Discriminator Loss: 0.6520... Generator Loss: 1.4881
Epoch 1/2... Discriminator Loss: 1.3307... Generator Loss: 0.6499
Epoch 1/2... Discriminator Loss: 1.2716... Generator Loss: 2.0140
Epoch 1/2... Discriminator Loss: 1.1751... Generator Loss: 0.6608
Epoch 1/2... Discriminator Loss: 0.6211... Generator Loss: 4.8032
Epoch 1/2... Discriminator Loss: 1.4956... Generator Loss: 0.4390
Epoch 1/2... Discriminator Loss: 0.8609... Generator Loss: 3.8225
Epoch 1/2... Discriminator Loss: 2.2156... Generator Loss: 0.2480
Epoch 1/2... Discriminator Loss: 1.5070... Generator Loss: 3.0305

Epoch 1/2... Discriminator Loss: 2.2640... Generator Loss: 0.2017
Epoch 1/2... Discriminator Loss: 1.6336... Generator Loss: 3.6643
Epoch 1/2... Discriminator Loss: 1.6573... Generator Loss: 0.4508
Epoch 1/2... Discriminator Loss: 0.6915... Generator Loss: 2.0972
Epoch 1/2... Discriminator Loss: 0.9541... Generator Loss: 1.6211
Epoch 1/2... Discriminator Loss: 0.9855... Generator Loss: 0.8578
Epoch 1/2... Discriminator Loss: 0.7826... Generator Loss: 1.8912
Epoch 1/2... Discriminator Loss: 0.9116... Generator Loss: 1.1837
Epoch 1/2... Discriminator Loss: 0.6672... Generator Loss: 1.3243
Epoch 1/2... Discriminator Loss: 0.8691... Generator Loss: 1.2798

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 64
z_dim = 100
learning_rate = 0.0004
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 1/1... Discriminator Loss: 8.3864... Generator Loss: 0.0004
Epoch 1/1... Discriminator Loss: 1.1948... Generator Loss: 0.6121
Epoch 1/1... Discriminator Loss: 0.6675... Generator Loss: 4.0216
Epoch 1/1... Discriminator Loss: 0.9010... Generator Loss: 1.0718
Epoch 1/1... Discriminator Loss: 0.4981... Generator Loss: 1.7252
Epoch 1/1... Discriminator Loss: 0.3604... Generator Loss: 2.5566
Epoch 1/1... Discriminator Loss: 0.2319... Generator Loss: 2.2490
Epoch 1/1... Discriminator Loss: 0.0745... Generator Loss: 4.7460
Epoch 1/1... Discriminator Loss: 0.0887... Generator Loss: 3.1831
Epoch 1/1... Discriminator Loss: 0.1965... Generator Loss: 2.3560

Epoch 1/1... Discriminator Loss: 0.1171... Generator Loss: 2.8187
Epoch 1/1... Discriminator Loss: 0.0565... Generator Loss: 5.5088
Epoch 1/1... Discriminator Loss: 0.0422... Generator Loss: 4.3627
Epoch 1/1... Discriminator Loss: 0.0364... Generator Loss: 3.9014
Epoch 1/1... Discriminator Loss: 0.0276... Generator Loss: 4.5667
Epoch 1/1... Discriminator Loss: 0.4263... Generator Loss: 1.3619
Epoch 1/1... Discriminator Loss: 0.0333... Generator Loss: 16.0758
Epoch 1/1... Discriminator Loss: 0.0783... Generator Loss: 16.1689
Epoch 1/1... Discriminator Loss: 0.0204... Generator Loss: 11.5002
Epoch 1/1... Discriminator Loss: 0.0076... Generator Loss: 8.2272

Epoch 1/1... Discriminator Loss: 0.0058... Generator Loss: 6.1308
Epoch 1/1... Discriminator Loss: 0.0204... Generator Loss: 4.3159
Epoch 1/1... Discriminator Loss: 0.0066... Generator Loss: 6.7407
Epoch 1/1... Discriminator Loss: 0.0081... Generator Loss: 5.5652
Epoch 1/1... Discriminator Loss: 0.1296... Generator Loss: 2.7608
Epoch 1/1... Discriminator Loss: 0.0222... Generator Loss: 15.2337
Epoch 1/1... Discriminator Loss: 0.0088... Generator Loss: 17.8468
Epoch 1/1... Discriminator Loss: 0.0090... Generator Loss: 16.0259
Epoch 1/1... Discriminator Loss: 0.0033... Generator Loss: 13.2216
Epoch 1/1... Discriminator Loss: 0.0025... Generator Loss: 8.3292

Epoch 1/1... Discriminator Loss: 0.0111... Generator Loss: 5.0896
Epoch 1/1... Discriminator Loss: 0.0139... Generator Loss: 4.8616
Epoch 1/1... Discriminator Loss: 0.0178... Generator Loss: 4.9546
Epoch 1/1... Discriminator Loss: 0.0345... Generator Loss: 3.9981
Epoch 1/1... Discriminator Loss: 0.0032... Generator Loss: 6.4046
Epoch 1/1... Discriminator Loss: 0.0252... Generator Loss: 4.5417
Epoch 1/1... Discriminator Loss: 0.1002... Generator Loss: 3.1962
Epoch 1/1... Discriminator Loss: 0.0123... Generator Loss: 7.0668
Epoch 1/1... Discriminator Loss: 0.0114... Generator Loss: 7.6410
Epoch 1/1... Discriminator Loss: 0.0364... Generator Loss: 4.6189

Epoch 1/1... Discriminator Loss: 0.0371... Generator Loss: 4.0623
Epoch 1/1... Discriminator Loss: 0.0226... Generator Loss: 5.0530
Epoch 1/1... Discriminator Loss: 0.2464... Generator Loss: 2.2714
Epoch 1/1... Discriminator Loss: 0.2608... Generator Loss: 17.2899
Epoch 1/1... Discriminator Loss: 0.0082... Generator Loss: 7.0111
Epoch 1/1... Discriminator Loss: 7.7125... Generator Loss: 0.0023
Epoch 1/1... Discriminator Loss: 3.5701... Generator Loss: 26.4469
Epoch 1/1... Discriminator Loss: 0.0361... Generator Loss: 20.1820
Epoch 1/1... Discriminator Loss: 1.2009... Generator Loss: 1.3634
Epoch 1/1... Discriminator Loss: 0.0916... Generator Loss: 5.3727

Epoch 1/1... Discriminator Loss: 1.5335... Generator Loss: 1.0253
Epoch 1/1... Discriminator Loss: 0.6681... Generator Loss: 6.5115
Epoch 1/1... Discriminator Loss: 0.3322... Generator Loss: 5.3584
Epoch 1/1... Discriminator Loss: 3.2562... Generator Loss: 0.2608
Epoch 1/1... Discriminator Loss: 0.7988... Generator Loss: 9.4702
Epoch 1/1... Discriminator Loss: 0.3173... Generator Loss: 3.6236
Epoch 1/1... Discriminator Loss: 2.8084... Generator Loss: 0.5311
Epoch 1/1... Discriminator Loss: 0.6944... Generator Loss: 5.6567
Epoch 1/1... Discriminator Loss: 0.5122... Generator Loss: 2.2624
Epoch 1/1... Discriminator Loss: 1.4044... Generator Loss: 0.6963

Epoch 1/1... Discriminator Loss: 0.5991... Generator Loss: 4.1124
Epoch 1/1... Discriminator Loss: 0.7573... Generator Loss: 1.4775
Epoch 1/1... Discriminator Loss: 1.1746... Generator Loss: 0.6921
Epoch 1/1... Discriminator Loss: 0.6429... Generator Loss: 3.4329
Epoch 1/1... Discriminator Loss: 0.8846... Generator Loss: 1.4050
Epoch 1/1... Discriminator Loss: 1.5487... Generator Loss: 0.6179
Epoch 1/1... Discriminator Loss: 0.8625... Generator Loss: 2.2787
Epoch 1/1... Discriminator Loss: 1.7535... Generator Loss: 0.5140
Epoch 1/1... Discriminator Loss: 0.9308... Generator Loss: 3.5095
Epoch 1/1... Discriminator Loss: 0.8831... Generator Loss: 1.2179

Epoch 1/1... Discriminator Loss: 1.0653... Generator Loss: 0.7432
Epoch 1/1... Discriminator Loss: 0.5595... Generator Loss: 2.6905
Epoch 1/1... Discriminator Loss: 1.7314... Generator Loss: 0.6695
Epoch 1/1... Discriminator Loss: 1.0621... Generator Loss: 3.9547
Epoch 1/1... Discriminator Loss: 1.5340... Generator Loss: 0.4369
Epoch 1/1... Discriminator Loss: 0.7767... Generator Loss: 4.1543
Epoch 1/1... Discriminator Loss: 0.5609... Generator Loss: 1.6049
Epoch 1/1... Discriminator Loss: 0.5511... Generator Loss: 1.3898
Epoch 1/1... Discriminator Loss: 0.5421... Generator Loss: 2.0875
Epoch 1/1... Discriminator Loss: 0.7740... Generator Loss: 1.1562

Epoch 1/1... Discriminator Loss: 0.7103... Generator Loss: 1.1005
Epoch 1/1... Discriminator Loss: 0.5624... Generator Loss: 2.9998
Epoch 1/1... Discriminator Loss: 0.9595... Generator Loss: 0.9036
Epoch 1/1... Discriminator Loss: 0.8184... Generator Loss: 6.2331
Epoch 1/1... Discriminator Loss: 0.2893... Generator Loss: 1.7756
Epoch 1/1... Discriminator Loss: 1.2053... Generator Loss: 0.5546
Epoch 1/1... Discriminator Loss: 0.5203... Generator Loss: 6.5100
Epoch 1/1... Discriminator Loss: 0.2213... Generator Loss: 4.7881
Epoch 1/1... Discriminator Loss: 1.0871... Generator Loss: 0.8974
Epoch 1/1... Discriminator Loss: 0.3248... Generator Loss: 2.7137

Epoch 1/1... Discriminator Loss: 0.3305... Generator Loss: 2.8473
Epoch 1/1... Discriminator Loss: 0.7025... Generator Loss: 1.0717
Epoch 1/1... Discriminator Loss: 0.5185... Generator Loss: 1.5423
Epoch 1/1... Discriminator Loss: 0.4560... Generator Loss: 2.3570
Epoch 1/1... Discriminator Loss: 2.1665... Generator Loss: 0.2081
Epoch 1/1... Discriminator Loss: 3.1028... Generator Loss: 10.1263
Epoch 1/1... Discriminator Loss: 1.4403... Generator Loss: 0.5763
Epoch 1/1... Discriminator Loss: 0.1346... Generator Loss: 3.0135
Epoch 1/1... Discriminator Loss: 0.1816... Generator Loss: 3.7544
Epoch 1/1... Discriminator Loss: 0.2449... Generator Loss: 2.4676

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.